DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. IN202321054974, filed on 08/16/2023.
Information Disclosure Statement
The information disclosure statement (IDS) was submitted on 07/26/2024. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Interpretation
Claims 1, 7, and 13 detail limitation with regards to a “residue”. The claim limitation is interpreted under broadest reasonable interpretation, with the dictionary definition from Merriam-Webster defines “residue” as “something that remains after a part is taken, separated, or designated or after the completion of a process”.
Claim Objections
Claims 1, 7, and 13 are objected to because of the following informalities:
Claims 1, 7, and 13 detail the limitation “wherein step of iteratively transforming the plurality of target domain data (Xt) comprises” which should read “wherein the step of iteratively transforming the plurality of target domain data (Xt) comprises.”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
The limitation of claims 1, 7, and 13 of “obtaining, via one or more hardware processors, (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection” details a first set of features (d) and a second set of features (d). As both sets of features are given by (d), it is not clear nor distinct whether the claim limitation is stating that the first set of features and the second set of features are the same or if they are different from each other. Examiner interprets the first set of features and second set of features as not being the same, and thus as the first set of features (ds) and the second set of features (dt).
Claims 2-6 and 8-12 are rejected due to dependence on Claims 1 and 7.
Claims 1-13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 7, and 13 detail the limitations as labelled below:
“(i) computing, a plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt) by transforming the plurality of target domain data (Xt) using a transform of current subspace”
“(ii) computing the residue on the plurality of target domain data (Xt) using a current transform and the plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt)”
“(iii) computing a change in transform of a current subspace to reduce the residue on the plurality of target domain data (Xt)”
“(iv) computing a transform of a subsequent subspace by adding the change in the transform to the transform of the current subspace”
Limitation (a) details without any antecedence a “current subspace”, limitation (c) details the limitation with the antecedence with “a” with “current subspace”, and limitation (d) details the antecedence with “the” with “current subspace”. Thus is it not clear not distinct whether the “current subspace” from the limitations of (a) is the same as that of (c), or whether the current subspace of (d) is in reference to that of (a) or (c). Examiner interprets the current subspace in all three limitations as the same current subspace.
Claims 2-6 and 8-12 are rejected due to dependence on Claims 1 and 7.
Claims 3 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 3 and 9 details the limitation “wherein each of the deep transform is indicative of number of subspace, and wherein the deep source domain transform (T0) indicates a first subspace, an intermediate deep transform (Tm) from the set of intermediate deep transforms indicates mth subspace, and the deep target transform (TM) indicates a target subspace.” It is not clear nor distinct which “deep transform” the claim limitation is in reference to, as Claim 1 (and 7-8) and Claim 3 (and 9) detail multiple deep transforms, including the set of intermediate deep transforms, the deep source domain transform, and deep target source domain transform. Examiner interprets the claim limitation as a single member of the set of intermediate deep transforms.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13 are rejected under 35 U.S.C. 101. The claimed invention is directed to the abstract concept of performing abstract steps without significantly more. The claim(s) recite(s) the following abstract concepts in BOLD of
1. A processor implemented method, comprising:
obtaining, via one or more hardware processors, (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection;
learning, via the one or more hardware processors, a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0);
computing, via the one or more hardware processors, a plurality of coefficients (Z0) on the plurality of source domain data (Xs) using the deep source domain transform (T0); and
learning, via one or more hardware processors, a set of intermediate deep transforms associated with a plurality of intermediate domains, wherein the set of intermediate deep transforms associated with the plurality of intermediate domains are learned by iteratively transforming the plurality of target domain data (Xt) along a direction that reduces a residue on the plurality of target domain data (Xt) till a deep target domain transform (TM) is obtained that best represents the plurality of target domain data (Xt), and wherein step of iteratively transforming the plurality of target domain data (Xt) comprises:
(i) computing, a plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt) by transforming the plurality of target domain data (Xt) using a transform of current subspace;
(ii) computing the residue on the plurality of target domain data (Xt) using a current transform and the plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt);
(iii) computing a change in transform of a current subspace to reduce the residue on the plurality of target domain data (Xt); and
(iv) computing a transform of a subsequent subspace by adding the change in the transform to the transform of the current subspace.
7. A system, comprising:
a memory storing instructions;
one or more communication interfaces; and
one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
obtain (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection;
learn a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0);
compute a plurality of coefficients (Z0) on the plurality of source domain data (Xs) using the deep source domain transform (T0); and
learn a set of intermediate deep transforms associated with a plurality of intermediate domains, wherein the set of intermediate deep transforms associated with the plurality of intermediate domains are learned by iteratively transforming the plurality of target domain data (Xt) along a direction that reduces a residue on the plurality of target domain data (Xt) till a deep target domain transform (TM) is obtained that best represents the plurality of target domain data (Xt), and wherein step of iteratively transforming the plurality of target domain data (Xt) comprises:
(i) computing, a plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt) by transforming the plurality of target domain data (Xt) using a transform of current subspace;
(ii) computing the residue on the plurality of target domain data (Xt) using a current transform and the plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt);
(iii) computing a change in transform of a current subspace to reduce the residue on the plurality of target domain data (Xt); and
(iv) computing a transform of a subsequent subspace by adding the change in the transform to the transform of the current subspace.
13. One or more non-transitory computer readable mediums comprising one or more instructions which when executed by one or more hardware processors cause:
obtaining (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection;
learning a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0);
computing a plurality of coefficients (Z0) on the plurality of source domain data (Xs) using the deep source domain transform (T0); and
learning a set of intermediate deep transforms associated with a plurality of intermediate domains, wherein the set of intermediate deep transforms associated with the plurality of intermediate domains are learned by iteratively transforming the plurality of target domain data (Xt) along a direction that reduces a residue on the plurality of target domain data (Xt) till a deep target domain transform (TM) is obtained that best represents the plurality of target domain data (Xt), and wherein step of iteratively transforming the plurality of target domain data (Xt) comprises:
(i) computing, a plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt) by transforming the plurality of target domain data (Xt) using a transform of current subspace;
(ii) computing the residue on the plurality of target domain data (Xt) using a current transform and the plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt);
(iii) computing a change in transform of a current subspace to reduce the residue on the plurality of target domain data (Xt); and
(iv) computing a transform of a subsequent subspace by adding the change in the transform to the transform of the current subspace.
Under step 1 of the eligibility analysis, we determine whether the claims are to a statutory category by considering whether the claimed subject matter falls within the four statutory categories of patentable subject matter identified by 35 U.S.C. 101: process, machine, manufacture, or composition of matter. The above claims are considered to be in a statutory category.
Under Step 2A, Prong One, we consider whether the claim recites a judicial exception (abstract idea). In the above claim, the highlighted portion constitutes an abstract idea because, under a broadest reasonable interpretation, it recites limitation the fall into/recite abstract idea exceptions. Specifically, under the 2019 Revised Patent Subject Matter Eligibility Guidance, it falls into the grouping of subject matter that, when recited as such in a claim limitation, covers performing mathematics or mental steps.
Next, under Step 2A, Prong Two, we consider whether the claim that recites a judicial exception is integrated into a practical application. In this step, we evaluate whether the claim recites additional elements that integrate the exception into a practical application of that exception.
This judicial exception is not integrated into a practical application because there is no improvement to another technology or technical field; improvements to the functioning of the computer itself; a particular machine; effecting a transformation or reduction of a particular article to a different state or thing. Examiner notes that since the claimed methods and system are not tied to a particular machine or apparatus, they do not represent an improvement to another technology or technical field. Similarly there are no other meaningful limitations linking the use to a particular technological environment. Finally, there is nothing in the claims that indicates an improvement to the functioning of the computer itself or transform a particular article to a new state.
Finally, under Step 2B, we consider whether the additional elements are sufficient to amount to significantly more than the abstract idea. Claims 1, 7, and 13 do not include additional elements that are sufficient to amount to significantly more than the judicial exception because a step of obtaining a plurality of source domain data and target domain data is considered necessary data gathering. As recited in MPEP section 2106.05(g), necessary data gathering (i.e. obtaining data) is considered extra solution activity in light of Mayo, 566 U.S. at 79, 101 USPQ2d at 1968; OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1092-93 (Fed. Cir. 2015). The hardware processor of Claims 1, 7 and 13 and the memory and communication of Claim 7 are interpreted under broadest reasonable interpretation to be generic computer elements. Generic computer elements are not considered significantly more than the abstract idea and do not integrate the abstract idea into a practical application. As recited in the MPEP, 2106.05(b), merely adding a generic computer, generic computer components, or a programmed computer to perform generic computer functions does not automatically overcome an eligibility rejection. Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 134 S. Ct. 2347, 2359-60, 110 USPQ2d 1976, 1984 (2014). See also OIP Techs. v. Amazon.com, 788 F.3d 1359, 1364, 115 USPQ2d 1090, 1093-94.
Claims 2-6 and 8-12 further limit the abstract ideas without integrating the abstract concept into a practical application or including additional limitations that can be considered significantly more than the abstract idea.
Examiner’s Note
Claims 1-13 are not rejected under a 35 U.S.C. 102 or 35 U.S.C. 103.
In regards to claims 1, 7, and 13, Gupta (US20200012889) teaches the limitations “a memory storing instructions; one or more communication interfaces (system with memory 102, i/o interface 106, and hardware processors 104 – [0025], Figure 1); and one or more hardware processors coupled to the memory via the one or more communication interfaces (system with memory 102, i/o interface 106, and hardware processors 104 – [0025], Figure 1), wherein the one or more hardware processors are configured by the instructions to:
obtain (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection (“ In an aspect, there is provided a processor implemented method for performing a model driven domain specific search comprising: identifying a source domain comprising one or more corrupted data (y) in a training set; identifying a target domain comprising one or more clean data (x) in the training set; and generating a model for coupled representation learning using equal number of the one or more corrupted data and the one or more clean data, the step of generating a model comprising: learning a source domain transform (TS ) and corresponding source domain features (ZS ) pertaining to the source domain; learning a target domain transform (TT ) and corresponding target domain features (ZT ) pertaining to the target domain; and coupling the source domain features (ZS ) and the target domain features (ZT ) using an objective function serving as a coupling mechanism based on the analysis sparse coding formulation and characterized by a coupling operator (A) such that the source domain features (ZS ) are linearly mapped to the target domain features (ZT ), wherein the objective function being a non-convex problem is solved using the alternating minimization method by dividing the non-convex problem into a plurality of convex problems, wherein the plurality of convex problems is solved in an alternating manner until the objective function converges to a local minima” – [0006]);
learn a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0) (learning a source domain transform (TS ) - [0006]);
compute a plurality of coefficients (Z0) on the plurality of source domain data (Xs) using the deep source domain transform (T0)(“In the context of the present disclosure, the expressions ‘source domain’ and ‘measurement domain’ may be used interchangeably. Likewise, ‘target domain’ and ‘signal domain’ may be used interchangeably. The expressions ‘basis’ or ‘transform’ may be used interchangeably. Furthermore, ‘features’ or ‘coefficients’ may be used interchangeably” – [0023]; “Dictionary learning is a synthesis formulation; sometimes referred as synthesis sparse coding. Transform learning used in the present disclosure is an analysis equivalent of dictionary learning. In analysis sparse coding, an analysis basis operates on the data to generate coefficients (features). Mathematically this may be expressed as: T=XZ where T is the analysis basis or transform, X is the data and Z the corresponding coefficient. The following formulation was disclosed in “Learning Sparsifying Transforms,” by S. Ravishankar et al. in the IEEE Transactions on Signal Processing vol. 61.” – [0034]).”
Ma (CN114330413A) teaches the limitations “obtain (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection (Each type of fault data lasts 360 seconds, is divided into 1800 equal parts, each part is 0.2 seconds long, and each part is labeled with the corresponding fault type. The eight types of fault data [i.e. features] were combined to form a total of 14,400 labeled datasets, which served as the dataset for the deep learning neural network. 11,520 data points (1,440 for each type of fault) were selected as training data [i.e. source domain data] for the network, and another 1,440 data points (180 for each type of fault) were selected as validation data for the network. The remaining 1440 data points (180 for each type of fault) were used as network test data [i.e. target domain data]. The obtained random set of vibration data waveforms [i.e. plurality of measurements] for each type of fault is shown in Figure 3. - [0048]; This invention relates to the field of bearing fault detection for traction motors, specifically a method for identifying and locating fault types in traction motor bearings [i.e. for machine inspection]. - [n0001]); learn a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0) (In this embodiment, the created deep learning network model consists of eleven different layers. The first layer is the signal input layer, which is used to input vibration signals into the network. Layers two through seven consist of six convolutional processing layers used to identify the characteristics of different faults. The second layer is the convolutional processing layer 1, which consists of a convolutional layer (CNN1), a batch normalization layer (BN1), a ReLU layer (ReLU1), and a max pooling layer (MaxPool1). The kernel size of this layer is set to 16, and the pooling size is 2. The third layer is the convolutional processing layer 2, which consists of a convolutional layer (CNN2), a batch normalization layer (BN2), a ReLU layer (ReLU2), and a max pooling layer (MaxPool2). The kernel size of this layer is set to 24, and the pooling size is 2. The fourth layer is the convolutional processing layer 3, which consists of a convolutional layer (CNN3), a batch normalization layer (BN3), a ReLU layer (ReLU3), and a max pooling layer (MaxPool3). The kernel size of this layer is set to 32, and the pooling size is 2. The fifth layer is the convolutional processing layer 4, which consists of a convolutional layer (CNN4), a batch normalization layer (BN4), a ReLU layer (ReLU4), and a max pooling layer (MaxPool4). The filter size of this layer is set to 48, and the pooling size is 2. The sixth layer is the convolutional processing layer 5, which consists of a convolutional layer (CNN5), a batch normalization layer (BN5), a ReLU layer (ReLU5), and a max pooling layer (MaxPool5). The filter size of this layer is set to 64, and the pooling size is 2. The sixth layer is the convolutional processing layer 6, which consists of a convolutional layer (CNN6), a batch normalization layer (BN6), a ReLU layer (ReLU6), and a max pooling layer (MaxPool6). The filter size of this layer is set to 96, and the pooling size is 2. - [0050])”
Zhong (CN114371008A) teaches the limitations “obtain (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection ( To achieve the above objectives, the technical solution of the present invention is: a method for cross-condition fault diagnosis of rolling bearings based on VMD multi-domain features and MEDA – [n0007]; Step S1: Collect rolling bearing vibration signals and construct source domain data and target domain data - [n0008]; Step S2: Use parameter-optimized VMD to perform VMD decomposition on the source domain data and target domain data, extract their singular value features and permutation entropy features, combine them with the time domain features of the original signal to form multi-domain features and normalize them. - [n0009])”
Clinchant (US20180024968) teaches the limitations “obtain (i) a plurality of source domain data (Xs) comprising a first set of features (d) with a plurality of measurements (ns), and (ii) a plurality of target domain data (Xt) comprising a second set of features (d) with a plurality of measurements (nt) for a machine inspection (“In accordance with another aspect of the exemplary embodiment, a system for domain adaptation includes a domain predictor for predicting a domain of a plurality of domains for a sample, the sample including values for a set of features, the plurality of domains including at least one source domain and a target domain. A mapping component receives a set of training samples from the plurality of domains. Each training sample includes values for the set of features. The training samples from each of the at least one source domain may each be labeled with labels of a set of class labels. The mapping component performs domain adaptation using marginalized denoising autoencoding on the set of training samples to generate at least one domain adaptation transform layer operative to transform the training samples to a common adapted feature space. The domain adaptation incorporates the domain predictor to bias the domain adaptation of the source domain training samples towards one of the plurality of domains. A processor implements the mapping component.” – [0013]); learn a deep source domain transform (T0) associated with the plurality of source domain data using a N-layer deep transform learning (DTL) architecture, wherein a transform for each layer of N-layer deep source domain transform (T0) is computed for learning the deep source domain transform (T0) (“The mapping component performs domain adaptation using marginalized denoising autoencoding on the set of training samples to generate at least one domain adaptation transform layer operative to transform the training samples to a common adapted feature space. The domain adaptation incorporates the domain predictor to bias the domain adaptation of the source domain training samples towards one of the plurality of domains. A processor implements the mapping component.” – [0013]; “As illustrated in FIG. 3, the sMDA 52 is a stack of t domain adaptation transform layers 80, 82 84, etc. (although a single layer may be used in one embodiment). Each layer includes a linear denoising autoencoder (MDA) 90, 92, 94, etc. Each autoencoder includes an encoder 96 and a decoder 98 (only the components of the first layer are shown for ease of illustration). Each encoder 96 takes as input a set X of representations and corrupts them by adding random noise to give a corrupted set of representations {tilde over (X)}. The decoder 98 then attempts to reconstruct the input representations producing, in the process, a reconstructed set of representations {circumflex over (X)}. This corruption and reconstruction can be replaced by a closed form solution, as described below.” – [0064]); learn a set of intermediate deep transforms associated with a plurality of intermediate domains, wherein the set of intermediate deep transforms associated with the plurality of intermediate domains are learned by iteratively transforming the plurality of target domain data (Xt) along a direction that reduces a residue on the plurality of target domain data (Xt) till a deep target domain transform (TM) is obtained that best represents the plurality of target domain data (Xt) (“As illustrated in FIG. 4, the learning of the mapping 72 (S106) may include, for at least one, or at least a plurality of iterations, optionally, generating a corrupted set of representations from the input set of representations (S202), which is not needed when the closed form described below is used, learning a mapping that minimizes a reconstruction error for the corrupted set (S204) when a reconstructed set is generated by applying the transformation to the corrupted set, and outputting an adapted (i.e., reconstructed) set of representations (S206). If at S208, more iterations are to be performed, the input set for a subsequent iteration is based on an adapted set of representations generated in a first of the iterations, optionally, after performing a non-linear operation (S210). Otherwise, the method proceeds from S208 to S212, where the learned mapping 72 and mapped samples are stored.” – [0055]; “In the following, the terms “optimization,” “minimization,” and similar phraseology are to be broadly construed as one of ordinary skill in the art would understand these terms. For example, these terms are not to be construed as being limited to the absolute global optimum value, absolute global minimum, and so forth. For example, minimization of a function may employ an iterative minimization algorithm that terminates at a stopping criterion before an absolute minimum is reached. It is also contemplated for the optimum or minimum value to be a local optimum or local minimum value.” – [0059]; “Denoising autoencoders and their stacked marginalization version (mSDA) are an effective method for unsupervised domain adaptation. By jointly denoising the source and target data, they can generate a common representation. Adding a domain prediction component to (mSDA) facilitates learning of domain invariant features, which can be achieved in an unsupervised way. However, instead of extracting domain invariant features, the exemplary method seeks to transform/denoise source samples in such a way that the feature distributions get closer to the target ones.” – [0060])”
Gupta, Ma, Zhong, and Clinchant are silent with regards to the language of “learn a set of intermediate deep transforms associated with a plurality of intermediate domains, wherein the set of intermediate deep transforms associated with the plurality of intermediate domains are learned by iteratively transforming the plurality of target domain data (Xt) along a direction that reduces a residue on the plurality of target domain data (Xt) till a deep target domain transform (TM) is obtained that best represents the plurality of target domain data (Xt), and wherein step of iteratively transforming the plurality of target domain data (Xt) comprises:
(i) computing, a plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt) by transforming the plurality of target domain data (Xt) using a transform of current subspace;
(ii) computing the residue on the plurality of target domain data (Xt) using a current transform and the plurality of coefficients (Zm) corresponding to the plurality of target domain data (Xt);
(iii) computing a change in transform of a current subspace to reduce the residue on the plurality of target domain data (Xt); and
(iv) computing a transform of a subsequent subspace by adding the change in the transform to the transform of the current subspace.”
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to YOSSEF KORANG-BEHESHTI whose telephone number is (571)272-3291. The examiner can normally be reached Monday - Friday 10:00 am - 6:30 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Catherine Rastovski can be reached at (571) 270-0349. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/YOSSEF KORANG-BEHESHTI/Primary Examiner, Art Unit 2857
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